Hi trubador. I have two series of interest rate and exchange rate, and would like to use dccgarch11 add-in to know the volatility spillovers between interest rate and exchange rate, and the dynamic correlation coefficients of them. Can you show me how to put in the four empty boxes, they are “return series”, “exogenous variables in the mean equation”, “number of AR lags to be used in mean equations”, and “exogenous variables in the variance equation”. Thanks much.

shuangqing wrote:Hi trubador. I have two series of interest rate and exchange rate, and would like to use dccgarch11 add-in to know the volatility spillovers between interest rate and exchange rate, and the dynamic correlation coefficients of them. Can you show me how to put in the four empty boxes, they are “return series”, “exogenous variables in the mean equation”, “number of AR lags to be used in mean equations”, and “exogenous variables in the variance equation”. Thanks much.

Hi Trubador, I am trying to find out the DCC for the tourism demand for singapore, south korea and thailand from Indonesia. However, my estimates from the DCC-GARCH(1,1) are not making sense. I get a single result instead of a time-varying correlation coefficient.Am i estimating it correctly using the add in?

For the mean eqn; I added 11 dummy monthly variables and AR(2) as encouraged by past literature.

alexlooyc wrote:Hi Trubador, I am trying to find out the DCC for the tourism demand for singapore, south korea and thailand from Indonesia. However, my estimates from the DCC-GARCH(1,1) are not making sense. I get a single result instead of a time-varying correlation coefficient.Am i estimating it correctly using the add in?

For the mean eqn; I added 11 dummy monthly variables and AR(2) as encouraged by past literature.

I have attached the workfile too!Pls assist!Thanks!

The output (i.e. dccout01) clearly indicates that the optimization was not successful. If you carefully examine your univariate GARCH estimates, you should see that the GARCH effects are very weak.

Please read the previous posts in this thread. We have had similar discussions before.

shuangqing wrote:Hi trubador. I have two series of interest rate and exchange rate, and would like to use dccgarch11 add-in to know the volatility spillovers between interest rate and exchange rate, and the dynamic correlation coefficients of them. Can you show me how to put in the four empty boxes, they are “return series”, “exogenous variables in the mean equation”, “number of AR lags to be used in mean equations”, and “exogenous variables in the variance equation”. Thanks much.

Have you seen the documentation that comes with the add-in?

Now I have got the documentation,and settled the problem. Think you, trubador.

One of the two is normally distributed, the other is not (given by Jarque-Bera)

Do you suggest using Normal or T_Stud distribution for this specific case? Thank you

There is nothing specific here. These distributions are fit to residuals, not to the series. So, you can estimate the model with the assumption of normal distribution and then check the (standardized) residuals to verify this. You can also test alternative empirical distributions to identify the best fit.

trubador wrote:1) Since this is a two-step model, you should check the first step (i.e. estimation of univariate GARCH models) to see if everything is OK. Try alternative GARCH models.2) Model may become ill-defined or inconsistent after including an exogenous variable or an AR term. Try dropping them or find better RHS variables.3) Starting values of coefficients may be too far from an optimal solution. Try different initial parameter values (i.e. theta vector).4) Correlation targeting may be too restrictive. Try unchecking this option.5) Sample period may not be appropriate to carry out such an analysis. Try adjusting the sample.6) Optimization algorithm may perform poorly. Try other alternatives.7) Algorithms may fall out the domain of estimation parameters. Try optimizing the squared coefficients.

Such models are nonlinear in nature and therefore there is no guarantee that they will always converge and yield proper estimation results. It really needs "your" time and effort to get it work.

Hi Trubador, I used the add-in for 5 time series. I think the results are fine?! I attached the work file. My question is, if its possible to get the variance covariance matrix for the time varying correlations to use the matrix for portfolio optimization. I did get it right that the rho_12_01 is the correlation between series 1 and 2 and so on?